This invention generally relates to the diagnostic testing for identifying individuals at risk for adverse effects from psychotropic or CNS-active treatments, and more particularly to using quantitative electroencephalography (QEEG) to identify individuals at risk for adverse antidepressant effects, such as suicidality.
Major depressive disorder (MDD) is a common illness, associated with high morbidity, mortality, and a very high economic cost for society. Treatment with antidepressant medications is associated with significant improvements of clinical symptoms of depression, as well as improvements of patients' functional status and quality of life. Due to the high prevalence of depression, millions in the U.S. alone are candidates for treatment with antidepressants every year.
Antidepressant medications have demonstrated efficacy for the symptoms of depression and, overall, antidepressant treatment is associated with improved mood and decreased suicidality. However, some individuals may experience worsening mood and suicidality during antidepressant treatment. For example, there is some evidence that antidepressant medications may be associated with increases in suicidal ideation and elevated risk for harm-related adverse events in a small subset of depressed individuals. Although the evidence is equivocal, concern over this matter has led the U.S. Food and Drug Administration (2004) to issue an advisory regarding worsening depression and suicidality for patients of all ages.
Absent any reliable means of identifying those individuals who are at greatest risk for experiencing such treatment-emergent adverse events (TEAEs), patients and prescribing doctors face uncertainty in how best to heed warnings of these potential risks of antidepressant treatment. The ability to identify those patients at highest risk for treatment-emergent worsening of suicidality and other adverse effects is an important unmet need.
Several studies have suggested associations between clinical symptoms of depression and treatment-emergent worsening of suicidality, but those results have been controversial and none has demonstrated clinically useful predictive capability for any clinical or biological indicator. In another area, a line of research has focused on brain functional biomarkers of treatment response in major depressive disorder (MDD) (Drevets et al 2002; Leuchter et al 1997, 2005; Mayberg et al 1997, 2000, 2003; Cook and Leuchter 2001; Cook et al 2002, 2005). However, (with two exceptions, i.e., Hunter et al, 2005 and Iosifescu et al, 2005), this work has focused on biomarkers of response or remission, and not for adverse side effects. Currently, clinicians are not able to identify who among their depressed patients is at risk for worsening suicidality or other adverse effects during antidepressant treatment.
There is a great deal of heterogeneity in pharmacotherapy outcomes and, as yet, no proven reliable means of predicting how an individual patient will fare during a given antidepressant treatment regimen. Whereas a great deal of research has focused on predicting dichotomous outcomes (e.g., response vs. non-response) at a primary endpoint, such outcomes do not address other clinically relevant issues related to the course of symptom changes prior to the endpoint. Of particular interest are patients who may experience either transient worsening of symptoms, i.e. “symptom volatility,” or more sustained clinical worsening, especially in the first few months after beginning antidepressant treatment.
It is therefore desirable to have methods, apparatus, and systems for efficiently and accurately identifying individuals at risk for adverse effects from psychotropic or CNS-active drugs.